- Protein differential expression analysis (DEA)
- DIANN
- FragPipe DDA
- FragPipe TMT
- MaxQuant
- Uses preprocessing and statistical models implemented in the R package
prolfqua
doi.org/10.1021/acs.jproteome.2c00441
- Generates dynamic HTML reports
- Exports results as XLSX files,
.rnk and .txt files for GSEA and ORA
How To
Install R and prolfquapp
install.packages('remotes')
remotes::install_github('wolski/prolfquapp', dependencies = TRUE)
Create a directory with :
- config.yaml (parameter file)
- dataset.csv (experimental design)
- the FASTA file
- DIANN, FragPipe or MaxQuant results
Copy the R code into the working directory by running one of the functions:

The content of the working directory is:

Finally, from R console source("FP_DIA.R"),
or execute Rscript FP_DIA.R. This
creates a subfolder with the DEA results.

- DE_Groups_vs_Controls.html report describing the main steps of the analysis and shows the results.
- DE_Groups_vs_Controls.xlsx contains the raw and transformed abundances, annotations, results of the differential expression analysis.
.rnk, and .txt files for GSEA and ORA analysis
The entire working directory is archived. It contains all the data and R code and data to replicate the analysis.
Analysis parameters
The config.yaml file specifies the parameters of the analysis:
- project related information e.g. projectID, is shown in the HTML report
- aggregation method
(medpolish, rlm, top_3)
- abundance transformation
(robscale, vsn, none),
- FDR and effect size thresholds

Sample annotation
The dataset.csv file contains the information about the measured samples:
- Relative.Path/raw.file/channel (unique)
- name - used in plots and figures (unique)
- group - main factor
- subject/bioreplicate (optional) - blocking factor
- control - used to specify the control condition (C) (optional)

If subject is specified then the model is abundance ~ group + subject, otherwise
abundance ~ group. The group differences to compute are determined from the group column and the control column.
HTML report
- Project related information (project ID etc)
- Primary introduction to DEA
- Sums up the design of the experiment
- Summarizes of protein ident. and quant.:
missigness, CV, clustering, PCA
- DEA results with volcano plots and tables (they interact using
crosslink)
- Explains outputs, give pointers to GSEA and ORA
- Additional QC report
Conlusion
- Integrates into LIMS system
doi.org/10.1515/jib-2022-0031
- Archived directory contains all information needed to replicate analysis
rerun the analysis on your PC
- Our users know Excel and like XLSX files
- Shiny app in development
